Subset Scanning algorithms [1]}, [2]}, [3]} identify the subset of records with the most significant difference in the distribution of the observed outcome compared to the expected outcome and have been applied across a variety of use cases. Neil et al. applied subset scanning in disease surveillance to detect geolocations and data streams suggestive of emerging outbreaks  [1]}, [5]}. Zheng et al. developed a subset scanning approach for detecting predictive bias in binary classifiers  [6]}. McFowland et al. proposed a subset scanning algorithm for detecting heterogeneous treatment effects in clinical trial data  [7]}. Somanchi et al. developed the anomalous patterns of care scan that combines propensity score modeling with subset scanning to enable anomalous pattern detection in anomalous pattern detection in observational health data [2]}. Each of these methods optimizes a scoring function, such as a log-likelihood ratio statistic, over all subsets in a dataset and selects the highest scoring subset. The scoring function is constrained to a mathematical property referred to as Linear-Time Subset Scanning (LTSS) that enables the search to be conducted in linear as opposed to exponential time  [1]}.
